[2603.25415] Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation

Computer Science > Artificial Intelligence arXiv:2603.25415 (cs) [Submitted on 26 Mar 2026] Title:Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation Authors:Roman Kueble, Marco Hueller, Mrunmai Phatak, Rainer Lienhart, Joerg Haehner View a PDF of the paper titled Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation, by Roman Kueble and 4 other authors View PDF HTML (experimental) Abstract:Semantic world models enable embodied agents to reason about objects, relations, and spatial context beyond purely geometric representations. In Organic Computing, such models are a key enabler for objective-driven self-adaptation under uncertainty and resource constraints. The core challenge is to acquire observations maximising model quality and downstream usefulness within a limited action budget. Semantic scene graphs (SSGs) provide a structured and compact representation for this purpose. However, constructing them within a finite action horizon requires exploration strategies that trade off information gain against navigation cost and decide when additional actions yield diminishing returns. This work presents a modular navigation component for Embodied Semantic Scene Graph Generation and modernises its decision-making by replacing the policy-optimisation method and revisiting the discrete action formulation. We study compact and finer-grained, larger discrete motion sets and compare a single-h...